Workload prediction is critical to intelligent data center functions, such as management and orchestration (M&O), as well as resource scheduling. This is because it is important for system administrators to properly manage and allocate resources for future demand in order to maintain quality of service levels in the data center (which are typically contractually agreed upon between the data center service provider and tenants of the data center via a service level agreement).
Advanced workload prediction has resorted to machine learning techniques. However, in data centers or cloud platforms, time-series application workloads often shift dramatically over time, invalidating a common assumption, held by common machine learning models, that the underlying process generating the data is stationary and that the data points are independent and identically distributed.
This issue is also known as concept drifting in machine learning. Concept drifting means that the statistical properties of a target variable change over time in unforeseen ways, which results in less accurate predictions produced as time passes.